maxent: Estimating Probabilities via Maximum Entropy: Improved...
In FD: Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology
maxent R Documentation
Estimating Probabilities via Maximum Entropy: Improved Iterative Scaling
Description
maxent returns the probabilities that maximize the entropy conditional on a series of constraints that are linear in the features. It relies on the Improved Iterative Scaling algorithm of Della Pietra et al. (1997). It has been used to predict the relative abundances of a set of species given the trait values of each species and the community-aggregated trait values at a site (Shipley et al. 2006; Shipley 2009; Sonnier et al. 2009).
Usage
maxent(constr, states, prior, tol = 1e-07, lambda = FALSE)
Arguments
constr
vector of macroscopical constraints (e.g. community-aggregated trait values). Can also be a matrix or data frame, with constraints as columns and data sets (e.g. sites) as rows.
states
vector, matrix or data frame of states (columns) and their attributes (rows).
prior
vector, matrix or data frame of prior probabilities of states (columns). Can be missing, in which case a maximally uninformative prior is assumed (i.e. uniform distribution).
tol
tolerance threshold to determine convergence. See ‘details’ section.
lambda
Logical. Should \lambda-values be returned?
Details
The biological model of community assembly through trait-based habitat
filtering (Keddy 1992) has been translated mathematically
via a maximum entropy (maxent) model by Shipley et al. (2006) and
Shipley (2009). A maxent model contains three components: (i) a set
of possible states and their attributes, (ii) a set of macroscopic empirical constraints,
and (iii) a prior probability distribution \mathbf{q}=[q_j].
In the context of community assembly, states are species, macroscopic
empirical constraints are community-aggregated traits, and prior probabilities
\mathbf{q} are the relative abundances of species of the regional
pool (Shipley et al. 2006, Shipley 2009). By default, these prior
probabilities \mathbf{q} are maximally uninformative (i.e. a uniform distribution),
but can be specificied otherwise (Shipley 2009, Sonnier et al. 2009).
To facilitate the link between the biological model and the mathematical
model, in the following description of the algorithm states are species and constraints are traits.
Note that if constr is a matrix or data frame containing several sets (rows),
a maxent model is run on each individual set. In this case if prior is a vector,
the same prior is used for each set. A different prior can also be specified for each set.
In this case, the number of rows in prior must be equal to the number of rows in constr.
If \mathbf{q} is not specified, set p_{j}=1/S for each of the
S species (i.e. a uniform distribution), where p_{j} is the
probability of species j, otherwise p_{j}=q_{j}.
Calulate a vector \mathbf{c=\left[\mathrm{\mathit{c_{i}}}\right]}=\{c_{1},\; c_{2},\;\ldots,\; c_{T}\},
where c_{i}={\displaystyle \sum_{j=1}^{S}t_{ij}}; i.e. each c_{i}
is the sum of the values of trait i over all species, and T
is the number of traits.
Repeat for each iteration k until convergence:
1. For each trait t_{i} (i.e. row of the constraint matrix) calculate:
\gamma_{i}(k)=ln\left(\frac{\bar{t}_{i}}{{\displaystyle \sum_{j=1}^{S}\left(p_{j}(k)\; t_{ij}\right)}}\right)\left(\frac{1}{c_{i}}\right)
This is simply the natural log of the known community-aggregated
trait value to the calculated community-aggregated trait value at
this step in the iteration, given the current values of the probabilities.
The whole thing is divided by the sum of the known values of the trait
over all species.
2. Calculate the normalization term Z:
Z(k)=\left({\displaystyle \sum_{j=1}^{S}p_{j}(k)\; e^{\left({\displaystyle \sum_{i=1}^{T}\gamma_{i}(k)}\; t_{ij}\right)}}\right)
3. Calculate the new probabilities p_{j} of each species at iteration k+1:
p_{j}(k+1)=\frac{{\displaystyle p_{j}(k)\; e^{\left({\displaystyle \sum_{i=1}^{T}\gamma_{i}(k)}\; t_{ij}\right)}}}{Z(k)}
4. If |max\left(p\left(k+1\right)-p\left(k\right)\right)|\leq tolerance threshold (i.e. argument tol) then stop, else repeat steps 1 to 3.
When convergence is achieved then the resulting probabilities (\hat{p}_{j})
are those that are as close as possible to q_j while simultaneously maximize
the entropy conditional on the community-aggregated traits. The solution to this problem is
the Gibbs distribution:
\hat{p}_{j}=\frac{q_{j}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}{{\displaystyle \sum_{j=1}^{S}q_{j}}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}=\frac{q_{j}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}{Z}
This means that one can solve for the Langrange multipliers (i.e.
weights on the traits, \lambda_{i}) by solving the linear system
of equations:
\left(\begin{array}{c}
ln\left(\hat{p}_{1}\right)\\
ln\left(\hat{p}_{2}\right)\\
\vdots\\
ln\left(\hat{p}_{S}\right)\end{array}\right)=\left(\lambda_{1},\;\lambda_{2},\;\ldots,\;\lambda_{T}\right)\left[\begin{array}{cccc}
t_{11} & t_{12} & \ldots & t_{1S}-ln(Z)\\
t_{21} & t_{22} & \vdots & t_{2S}-ln(Z)\\
\vdots & \vdots & \vdots & \vdots\\
t_{T1} & t_{T2} & \ldots & t_{TS}-ln(Z)\end{array}\right]-ln(Z)
This system of linear equations has T+1 unknowns (the T values
of \lambda plus ln(Z)) and S equations. So long as the number
of traits is less than S-1, this system is soluble. In fact, the
solution is the well-known least squares regression: simply regress
the values ln(\hat{p}_{j}) of each species on the trait values
of each species in a multiple regression.
The intercept is the value of ln(Z) and the slopes are the values
of \lambda_{i} and these slopes (Lagrange multipliers) measure
by how much the ln(\hat{p}_{j}), i.e. the ln(relative abundances),
changes as the value of the trait changes.
maxent.test provides permutation tests for maxent models (Shipley 2010).
Value
prob
vector of predicted probabilities
moments
vector of final moments
entropy
Shannon entropy of prob
iter
number of iterations required to reach convergence
lambda
\lambda-values, only returned if lambda = T
constr
macroscopical constraints
states
states and their attributes
prior
prior probabilities
Author(s)
Bill Shipley bill.shipley@usherbrooke.ca
http://www.billshipley.recherche.usherbrooke.ca/
Ported to FD by Etienne Laliberté.
References
Della Pietra, S., V. Della Pietra, and J. Lafferty (1997) Inducing features of random fields. IEEE Transactions Pattern Analysis and Machine Intelligence 19:1-13.
Keddy, P. A. (1992) Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3:157-164.
Shipley, B., D. Vile, and É. Garnier (2006) From plant traits to plant communities: a statistical mechanistic approach to biodiversity. Science 314: 812–814.
Shipley, B. (2009) From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities. Cambridge University Press, Cambridge, UK. 290 pages.
Shipley, B. (2010) Inferential permutation tests for maximum entropy models in ecology. Ecology in press.
Sonnier, G., Shipley, B., and M. L. Navas. 2009. Plant traits, species pools and the prediction of relative abundance in plant communities: a maximum entropy approach. Journal of Vegetation Science in press.
See Also
functcomp to compute community-aggregated traits,
and maxent.test for the permutation tests proposed by Shipley (2010).
Another faster version of maxent for multicore processors called maxentMC is available from Etienne Laliberté (etiennelaliberte@gmail.com). It's exactly the same as maxent but makes use of the multicore, doMC, and foreach packages. Because of this, maxentMC only works on POSIX-compliant OS's (essentially anything but Windows).
Examples
# an unbiased 6-sided dice, with mean = 3.5
# what is the probability associated with each side,
# given this constraint?
maxent(3.5, 1:6)
# a biased 6-sided dice, with mean = 4
maxent(4, 1:6)
# example with tussock dataset
traits <- tussock$trait[, c(2:7, 11)] # use only continuous traits
traits <- na.omit(traits) # remove 2 species with NA's
abun <- tussock$abun[, rownames(traits)] # abundance matrix
abun <- t(apply(abun, 1, function(x) x / sum(x) )) # relative abundances
agg <- functcomp(traits, abun) # community-aggregated traits
traits <- t(traits) # transpose matrix
# run maxent on site 1 (first row of abun), all species
pred.abun <- maxent(agg[1,], traits)
## Not run:
# do the constraints give predictive ability?
maxent.test(pred.abun, obs = abun[1,], nperm = 49)
## End(Not run)
FD documentation built on Nov. 26, 2023, 5:07 p.m.
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| maxent | R Documentation |
Estimating Probabilities via Maximum Entropy: Improved Iterative Scaling
Description
maxent returns the probabilities that maximize the entropy conditional on a series of constraints that are linear in the features. It relies on the Improved Iterative Scaling algorithm of Della Pietra et al. (1997). It has been used to predict the relative abundances of a set of species given the trait values of each species and the community-aggregated trait values at a site (Shipley et al. 2006; Shipley 2009; Sonnier et al. 2009).
Usage
maxent(constr, states, prior, tol = 1e-07, lambda = FALSE)
Arguments
constr |
vector of macroscopical constraints (e.g. community-aggregated trait values). Can also be a matrix or data frame, with constraints as columns and data sets (e.g. sites) as rows. |
states |
vector, matrix or data frame of states (columns) and their attributes (rows). |
prior |
vector, matrix or data frame of prior probabilities of states (columns). Can be missing, in which case a maximally uninformative prior is assumed (i.e. uniform distribution). |
tol |
tolerance threshold to determine convergence. See ‘details’ section. |
lambda |
Logical. Should |
Details
The biological model of community assembly through trait-based habitat
filtering (Keddy 1992) has been translated mathematically
via a maximum entropy (maxent) model by Shipley et al. (2006) and
Shipley (2009). A maxent model contains three components: (i) a set
of possible states and their attributes, (ii) a set of macroscopic empirical constraints,
and (iii) a prior probability distribution \mathbf{q}=[q_j].
In the context of community assembly, states are species, macroscopic
empirical constraints are community-aggregated traits, and prior probabilities
\mathbf{q} are the relative abundances of species of the regional
pool (Shipley et al. 2006, Shipley 2009). By default, these prior
probabilities \mathbf{q} are maximally uninformative (i.e. a uniform distribution),
but can be specificied otherwise (Shipley 2009, Sonnier et al. 2009).
To facilitate the link between the biological model and the mathematical model, in the following description of the algorithm states are species and constraints are traits.
Note that if constr is a matrix or data frame containing several sets (rows),
a maxent model is run on each individual set. In this case if prior is a vector,
the same prior is used for each set. A different prior can also be specified for each set.
In this case, the number of rows in prior must be equal to the number of rows in constr.
If \mathbf{q} is not specified, set p_{j}=1/S for each of the
S species (i.e. a uniform distribution), where p_{j} is the
probability of species j, otherwise p_{j}=q_{j}.
Calulate a vector \mathbf{c=\left[\mathrm{\mathit{c_{i}}}\right]}=\{c_{1},\; c_{2},\;\ldots,\; c_{T}\},
where c_{i}={\displaystyle \sum_{j=1}^{S}t_{ij}}; i.e. each c_{i}
is the sum of the values of trait i over all species, and T
is the number of traits.
Repeat for each iteration k until convergence:
1. For each trait t_{i} (i.e. row of the constraint matrix) calculate:
\gamma_{i}(k)=ln\left(\frac{\bar{t}_{i}}{{\displaystyle \sum_{j=1}^{S}\left(p_{j}(k)\; t_{ij}\right)}}\right)\left(\frac{1}{c_{i}}\right)
This is simply the natural log of the known community-aggregated trait value to the calculated community-aggregated trait value at this step in the iteration, given the current values of the probabilities. The whole thing is divided by the sum of the known values of the trait over all species.
2. Calculate the normalization term Z:
Z(k)=\left({\displaystyle \sum_{j=1}^{S}p_{j}(k)\; e^{\left({\displaystyle \sum_{i=1}^{T}\gamma_{i}(k)}\; t_{ij}\right)}}\right)
3. Calculate the new probabilities p_{j} of each species at iteration k+1:
p_{j}(k+1)=\frac{{\displaystyle p_{j}(k)\; e^{\left({\displaystyle \sum_{i=1}^{T}\gamma_{i}(k)}\; t_{ij}\right)}}}{Z(k)}
4. If |max\left(p\left(k+1\right)-p\left(k\right)\right)|\leq tolerance threshold (i.e. argument tol) then stop, else repeat steps 1 to 3.
When convergence is achieved then the resulting probabilities (\hat{p}_{j})
are those that are as close as possible to q_j while simultaneously maximize
the entropy conditional on the community-aggregated traits. The solution to this problem is
the Gibbs distribution:
\hat{p}_{j}=\frac{q_{j}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}{{\displaystyle \sum_{j=1}^{S}q_{j}}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}=\frac{q_{j}e^{\left({\displaystyle -}{\displaystyle \sum_{i=1}^{T}\lambda_{i}t_{ij}}\right)}}{Z}
This means that one can solve for the Langrange multipliers (i.e.
weights on the traits, \lambda_{i}) by solving the linear system
of equations:
\left(\begin{array}{c}
ln\left(\hat{p}_{1}\right)\\
ln\left(\hat{p}_{2}\right)\\
\vdots\\
ln\left(\hat{p}_{S}\right)\end{array}\right)=\left(\lambda_{1},\;\lambda_{2},\;\ldots,\;\lambda_{T}\right)\left[\begin{array}{cccc}
t_{11} & t_{12} & \ldots & t_{1S}-ln(Z)\\
t_{21} & t_{22} & \vdots & t_{2S}-ln(Z)\\
\vdots & \vdots & \vdots & \vdots\\
t_{T1} & t_{T2} & \ldots & t_{TS}-ln(Z)\end{array}\right]-ln(Z)
This system of linear equations has T+1 unknowns (the T values
of \lambda plus ln(Z)) and S equations. So long as the number
of traits is less than S-1, this system is soluble. In fact, the
solution is the well-known least squares regression: simply regress
the values ln(\hat{p}_{j}) of each species on the trait values
of each species in a multiple regression.
The intercept is the value of ln(Z) and the slopes are the values
of \lambda_{i} and these slopes (Lagrange multipliers) measure
by how much the ln(\hat{p}_{j}), i.e. the ln(relative abundances),
changes as the value of the trait changes.
maxent.test provides permutation tests for maxent models (Shipley 2010).
Value
prob |
vector of predicted probabilities |
moments |
vector of final moments |
entropy |
Shannon entropy of |
iter |
number of iterations required to reach convergence |
lambda |
|
constr |
macroscopical constraints |
states |
states and their attributes |
prior |
prior probabilities |
Author(s)
Bill Shipley bill.shipley@usherbrooke.ca
http://www.billshipley.recherche.usherbrooke.ca/
Ported to FD by Etienne Laliberté.
References
Della Pietra, S., V. Della Pietra, and J. Lafferty (1997) Inducing features of random fields. IEEE Transactions Pattern Analysis and Machine Intelligence 19:1-13.
Keddy, P. A. (1992) Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3:157-164.
Shipley, B., D. Vile, and É. Garnier (2006) From plant traits to plant communities: a statistical mechanistic approach to biodiversity. Science 314: 812–814.
Shipley, B. (2009) From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities. Cambridge University Press, Cambridge, UK. 290 pages.
Shipley, B. (2010) Inferential permutation tests for maximum entropy models in ecology. Ecology in press.
Sonnier, G., Shipley, B., and M. L. Navas. 2009. Plant traits, species pools and the prediction of relative abundance in plant communities: a maximum entropy approach. Journal of Vegetation Science in press.
See Also
functcomp to compute community-aggregated traits,
and maxent.test for the permutation tests proposed by Shipley (2010).
Another faster version of maxent for multicore processors called maxentMC is available from Etienne Laliberté (etiennelaliberte@gmail.com). It's exactly the same as maxent but makes use of the multicore, doMC, and foreach packages. Because of this, maxentMC only works on POSIX-compliant OS's (essentially anything but Windows).
Examples
# an unbiased 6-sided dice, with mean = 3.5
# what is the probability associated with each side,
# given this constraint?
maxent(3.5, 1:6)
# a biased 6-sided dice, with mean = 4
maxent(4, 1:6)
# example with tussock dataset
traits <- tussock$trait[, c(2:7, 11)] # use only continuous traits
traits <- na.omit(traits) # remove 2 species with NA's
abun <- tussock$abun[, rownames(traits)] # abundance matrix
abun <- t(apply(abun, 1, function(x) x / sum(x) )) # relative abundances
agg <- functcomp(traits, abun) # community-aggregated traits
traits <- t(traits) # transpose matrix
# run maxent on site 1 (first row of abun), all species
pred.abun <- maxent(agg[1,], traits)
## Not run:
# do the constraints give predictive ability?
maxent.test(pred.abun, obs = abun[1,], nperm = 49)
## End(Not run)
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